Method and architecture for compressing image data acquired from a bayer color filter array

ABSTRACT

For each color channel, the process includes gathering Bayer pattern pixel values by pairs, each pair being composed by two successive pixels belonging to the channel along the scanning direction of the pixels of the image, thus each pair of values representing a current input vector, and calculating a predictor vector of the input vector in terms of the differences between the values defining the input vector and a pair of prediction values generated according to a certain criterion, for representing a prediction error. The process further includes quantizing each so calculated predictor vector according to a heavier or lighter degree of quantization depending on whether the predictor vector is representative of an area of relatively uniform color of the image or of an area of relatively abrupt changes of colors of the image, and generating a multibit code representative of the quantized predictor vector of the input vector according to a certain compression ratio.

FIELD OF THE INVENTION

The present invention relates to image acquisition and image dataprocessing methods and devices. More particularly, the invention relatesto a pipeline for generating a high quality color image by carrying outinterpolation of chrominance data of pixel data coming from an imagesensor for outputting interpolated image pixel data.

BACKGROUND OF THE INVENTION

Generally, when using a video camera or a digital still camera tophotograph a color image, the incident light passes through filters forextracting certain wavelength components such as the basic colorcomponents R (red), G (green) and B (blue). In two-dimensional imaging,the imaging unit is composed of many pixels arranged in the vertical andhorizontal directions. Each pixel of the two-dimensional image containeither red, green or blue color light because of the filtering of theincident light. A color sensing array of this type is disclosed in thedocument U.S. Pat. No. 3,971,065 (B. E. Bayer).

According to one of several alternative techniques, the type of filteris changed for every pixel and the filters are cyclically aligned in theorder: G, R, G, R . . . in the even rows of the pixel array of thesensor, and B, G, B, G . . . in the odd rows of the pixel array of thesensor. As a consequence, information of the photographed colored objectis obtained only once every three pixels. In other words, an objectcannot be color photographed other than in units of three pixels.

To reconstruct all the pixels of the two-dimensional image of thephotographed object, it is necessary to interpolate chrominance pixeldata to obtain the color components of red, green and blue color usinginformation contained in neighboring pixels of the pixel to bereconstructed/enhanced. Generally, a value corresponding to theinterpolated pixel is reconstructed by averaging corresponding values ofa plurality of pixels surrounding the location of the pixel to beinterpolated. Alternatively, the interpolated pixel may be determined byaveraging the values of the pixels remaining after discarding pixels ofmaximum and minimum values of the neighbor pixels of the pixel to beinterpolated. Also well known are techniques for detecting an edge of aphotographical object by analyzing the pixels surrounding the consideredcluster.

U.S. Pat. No. 5,373,322, U.S. Pat. No. 5,053,861, U.S. Pat. No.5,040,064, U.S. Pat. No. 6,642,962, U.S. Pat. No. 6,570,616, U.S.published Patent Application No. 2003/0053687, U.S. Published PatentApplication No. 2003/0007082, U.S. published Patent Application No.2002/0101524, U.S. Pat. No. 6,366,694, European Patent Publication No. 0497 493, European Patent Publication No. 1 176 550, and European PatentPublication No. 1 406 447, disclose techniques that are used in imageprocessing.

Generally, the data acquired by the sensor according to a specialpattern, for example the one known in the art as Bayer color-filterarray (CFA), a pattern of which is characterized by associating just oneof the three basic color components to each pixel, therefore a goodquality RGB image is obtained by a specific image processing sequence(via hardware or image generation pipeline (IGP) or via software) togenerate a high quality color image. Generally, in cascade of such animage processing subsystem is associated a data compressing block forreducing the band necessary for transmitting the color reconstructedimage from the image processing subsystem or a mass storage support orto a remote receiver or to a display unit.

Data, for example in Bayer format, as acquired by a digital sensorclearly represent a gross approximation of the chromatic components ofthe reproduced scene, and it is of a paramount importance the accuracywith which color reconstruction via interpolation algorithms isperformed on the raw chrominance data acquired by the digital sensor.Usefulness of Bayer data compression has emerged quite recently. Sinceit requires a relatively inexpensive solution, both in terms ofcomputational complexity and hardware (HW) requirements, the most commonway to address the problem has been to split Bayer image color channelsand compress them independently using an efficient compressionalgorithm, for example differential pulse code modulation (DPCM), e.g.as discussed in R. M. Gray, D. L. Neuhoff, “Quantization”, IEEE Trans.on Information Theory, vol. 44, n. 6, October 1998.

Acharya et al. (U.S. Pat. No. 6,154,493) have proposed a rathersophisticated compression method for images in Bayer pattern format.According to their approach the Bayer pattern image is considered ascontaining four independent color planes. In fact, since there are twiceas many green related pixels as either of blue or red pixels, twodistinct green planes are constructed mainly: G1 (containing greenpixels in the same row as red pixels) and G2 (containing green pixels inthe same row as blue). B and R color planes represent blue and redpixels respectively.

To exploit advantageously both the correlation between an R associatedpixel and its G1 associated neighboring pixels and the correlationbetween associated pixel and its G2 and B associated neighboring pixels,compression is performed distinctly on each plane. G1 and G2 associatedpixels are compressed directly, while each R pixel value is subtractedby its “west” neighboring G1pixel value. Likewise, the difference (B-G2)is computed and planes (R-G1) and (B-G2) are then compressed.Compression is obtained in two main steps. Firstly, a 2-dimensionalDiscrete Wavelet Transform (DWT) is applied and secondly, DWTcoefficients are quantized. DWT data are used because make possible todescribe abrupt changes better then Fourier transform data.

The result is a lossy compression that is perceived by Human VisualSystem (HVS) to be lossless when decompressing is done. Decodingconsists simply on inverting the coding steps: data are dequantized andthen the Inverse DWT (IDWT) is performed. The four color channels may beseparately decompressed. Once IDWT is performed, by adding back G1 to(R-G1) recovered value and G2 to (B-G2) recovered value, each Bayeroriginal pixel value is restored.

Another sub band-coding compression method is described in T. Toi, M.Ohita, “A Sub band Coding Technique for Image Compression in Single CCDCameras with Bayer Color Filter Arrays”, IEEE Transaction on ConsumerElectronics, Vol. 45, N. 1, pp. 176-180, February 1999.

Lee and Ortega proposed an algorithm for Bayer image compression basedon Jpeg as discussed in Sang-Yong Lee, A. Ortega, “A Novel Approach ofImage Compression in Digital Cameras With a Bayer Color Filter Array”,In Proceedings of ICIP 2001—International Conference on ImageProcessing—Vol. III 482-485—Thessaloniki, Greece, October 2001.

Most digital cameras yield full color image by compressing an IGP imageinto a Jpeg image, after the color interpolation process performed bythe IGP pipeline, but in this way in the interpolation step redundancyis increased, before being reduced by Jpeg compression. To overcome thisdrawback, the algorithm performs an image transformation to encode theimage as a Jpeg image before color interpolation. The algorithm includesthree basic steps. A pre-processing of the image to convert Bayer datato YCbCr format with 4:2:2 or 4:2:0 sub sampling. The size of colorimage is, of course, three times bigger than that the Bayer data and, toavoid increasing redundancy, the size of data should not be increasedafter color format conversion. Thus, conversion is done as follows: eachY data includes common blue and red pixels and a different green pixel,while Cb and Cr data includes blue, red and the average of two properlychosen green pixels. After this transformation, Y data presents blankpixels, therefore Jpeg compression cannot be directly applied.Therefore, the second step is another transformation that simply rotatesthe Y data 45°.

After rotation, Y data are concentrated at the center of the image ofrhombus shape by removing rows and columns that contain blank pixels.Finally, Jpeg compression is performed. Blocks located along theboundaries of Y image data are filled by using a mirroring method. OtherBayer data compression techniques are described in U.S. Pat. No.5,172,227 to Tsai et al. and entitled “Image Compression with ColorInterpolation for a Single Sensor Image System” and Le Gall, A.Tabatabai, Subband Coding of Digital Images Using Symmetric Shor KernelFilters and Arithmetic Coding Techniques, in Proceedings of the ICASSP88 Conference, (New York), pp. 761-764, April 1988.

Vitali A., Della Torre L., Battiato S., Buemi A. in “Video and ImageLossy De/Compression By Perceptual Vector Quantization”, EP-A-1406447disclose a compression technique based on Vector Quantization whereinthe quantization step varies taking into account certain subjectivequality sensibility characteristics of the Human Visual System. Bayerpattern values are gathered according to the channel they belong to ingroups of two pixels and the so generated couple is quantized with afunction that accounts for the effects of “Edge Masking” and “LumaMasking”.

Modestino et al. describe in “Adaptive Entropy-Coded Predictive VectorQuantization of Images” IEEE Transaction on Signal Processing, Vol 40 No3, March 1992 discuss two dimensional predictive vector quantization ofimages subject to an entropy restraint.

Although the source data format of a Bayer pattern is amenable tocompression with relatively simple means and with a relatively lowcomputational burden, the known image compression algorithms so farproposed require a non-negligible computational complexity and arelatively large external memory requirement.

SUMMARY OF THE INVENTION

It is an object of the present invention to provide a simple yet moreefficient manner of compressing video or still image Bayer pattern pixeldata that would eliminate processing redundancies and imply apractically negligible computational complexity, such to allow an simplehardware implementation.

A particularly efficient and simple method for Bayer color filter array(CFA) compression has been found that while yielding a substantiallylossless performance of the compression-decompression processes,requires practically negligible computational complexity and externalmemory requirement.

According to this invention, the method of compressing video or stillimage Bayer pattern pixel data to be successively processed to a fullcolor image through color interpolation of each pixel of the full colorimage, comprises splitting the Bayer data in three distinct sequences orchannels of pixel data, one for each basic color filtered pixels of theimage, and of compressing separately each channel data according to aselected compression process. The method includes: for each colorchannel, gathering Bayer pattern pixel values by pairs, each pair beingcomposed by two successive pixels belonging to the channel along thescanning direction of the pixels of the image, thus each pair of valuesrepresenting a current input vector; calculating a predictor vector ofthe input vector in terms of the differences between the values definingthe input vector and a pair of prediction values generated according toa certain criterion, for representing a prediction error; quantizingeach so calculated predictor vector according to a heavier or lighterdegree of quantization depending on whether the predictor vector isrepresentative of an area of relatively uniform color of the image or ofan area of relatively abrupt changes of colors of the image; andgenerating a multibit code representative of the quantized predictorvector of the input vector according to a certain compression ratio.

Recognition of the type of image area of which the current predictorvector is representative is attained in an efficient manner by dividingthe input vectorial space in regions, all subdivided in an identicalnumber of equal areas, and having increasingly larger dimensions as theybecome more and more remote from the origin, and by mapping eachpredictor vector to a target vector of a set of target vectors,according to the region of division of the whole input vectorial space(domain) on which the predictor vector falls.

In consideration of the Human Visual System (HVS) that establishes a farmore enhanced sensitivity to color variations occurring in relativelyuniform areas of the image that is in areas where there are not edges orboundaries of objects implying abrupt color changes, then in areas ofthe image where numerous close by boundaries are present, according toan important aspect of the method of this invention, predictor vectorsquantization (VQ) is performed in a way to match the HVScharacteristics. This enhances efficiency of the compression processwhile ensuring a practically lossless performance.

In the sample embodiment illustrated, the input vectorial space isdivided in regions of different dimensions, while each region is dividedin a same number (e.g. 64) of sub-regions of identical areas, bydividing its horizontal and vertical dimension, for example, by eight.Therefore, regions of larger dimensions will be associated to aproportionately stronger quantization while regions of smallerdimensions will be associated to a proportionately reduced quantization,such that a greater proportion of information is preserved in thecompression process.

This, coupled to the fact that the differential pulse code modulation(DPCM) step is conducted on pairs of values of relatively neighboringpixels of one of the channels in which the pixels of the original imagehave been grouped, the prediction errors that are generated are rathersmall, and vector quantization may be performed with a substantiallynegligible computational complexity.

In terms of hardware implementability, the compression process may beimplemented by the use of simple logic gates, requiring no complexdivisions or shift operations. The method of this invention may also beeasily implemented with a software computer program.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram illustrating the DPCM+VQ compressionprocess of this invention;

FIG. 2 is a chart showing a typical prediction error distribution;

FIG. 3 is a schematic block diagram illustrating a preferred approach ofmapping of predictor vectors on the vectorial plane;

FIG. 4 is a table illustrating a sample division of the quantizationtable in distinct regions of different dimensions;

FIG. 5 is a table illustrating an enlarged detail representation of thedivision of the innermost zone of the quantization table;

FIG. 6 is a schematic diagram illustrating a sample subdivision of eachquantization region;

FIG. 7 is a schematic diagram illustrating the mechanism of multibitcode composition; and

FIG. 8 is a schematic block diagram illustrating the decompressionprocess.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The source data is organized according to a Bayer color filter array(CFA) or briefly to a Bayer pattern, and the data sequences to beseparately encoded are the pixel values belonging to each one of thethree basic colors channels (R,G,B). FIG. 1 illustrates the method ofDPCM+VQ compression process, according to this invention.

DPCM:

As depicted in the approach of FIG. 1, the input Bayer data are scannedline by line. The values P_(i) and P_(i+1) (that are two successivepixels the same color channel of the Bayer image) are gathered in pairs(or couples) and for each so defined current input vector P_(i),P_(i+1), prediction values, P_(i)′, P_(i+1)′ are generated to calculatethe differences or prediction errors e_(i), e_(i+1) that define what iscalled the predictor vector of the current vector. The DPCM step thatprecedes the VQ step exploits the high spatial correlation amongadjacent pixels, to perform compression by coding just the newinformation between adjacent pels. In particular, the “new information”is given by the prediction errors.

Generation of such a predictor vector may be established in anyappropriate manner. In the simplest implementation, the predicted valuesfor the successive pairs of values may be assumed to be the current pairof values, or two identical values equal to the second value of thecurrent pair of values (in consideration of the fact that the secondvalue of the current pair is that of a pixel spatially closer to thenext pairs of values (pixels) belonging to the same color channel).

A typical error distribution of such predictor vectors is shown in FIG.2. According to an aspect of the method of this invention, inconsidering that a relatively large percentile of values fall near theorigin, the Vector Quantization step that follows is aimed toefficiently match such a distribution.

The DPCM loop includes computing the difference between the vectorrepresenting the current pair of pixel to be coded and the predictionvalue. To avoid error propagation, the coding the prediction is obtainedfrom the restored values {circumflex over (P)}′_(i−2,){circumflex over(P)}′_(i−1), outputted by the co/decoding procedure applied to theoriginal values P′_(i−2), P′_(i−1). The prediction errors e_(i), e_(i+1)are given by the difference between the vectors P_(i) and P_(i+1) and{circumflex over (P)}′_(i−2,){circumflex over (P)}′_(i−1). Thus, theencoder uses the same data of the decoder and the prediction errordoesn't affect the following prediction.

Moreover, since the error distribution is symmetric respect to theorigin (FIG. 2), just the upper side is used to perform the coding. Onebit, called “inversion flag” will be used in the final 12-bits code toknow in which side of the space each prediction error falls. The “VectorMapping” block performs this step, while the “Vector Quantization” phaseallows obtaining the compression, as discussed in the followingsections.

Vector Quantization:

Each predictor vector e_(i), e_(i+1) is mapped to a set of targetvectors, also called codevectors in literature, according to the regionof the input vectorial space in which the vector falls and isaccordingly quantized in the block of “Vector Quantization” to produce aquantized error vector: {overscore (e)}_(i), {overscore (e)}_(i+1).Quantization will discard information according to psycho-visualconsiderations.

Each codevector is associated to a respective region of division of theinput vectorial space that gather all the input predictor vectors thatwill be mapped in it. According to what has been already explainedabove, it is therefore possible to attribute to the input predictorvector a certain output codevector without calculating the distance ofthe input vector from all the codevectors. If all the regions haveidentical dimensions, the quantization is uniform. For the purpose ofthe particular application considered, it is productive to vary thedimensions of the various regions to implement a different degree ofquantization in function of the location of a region in the input spaceas referred to the origin matching the peculiar sensibility tovariations of the Human Visual System.

By considering that the DPCM loop concentrates values in theneighborhood of the origin (zero), a very high percentile of values willfall in regions of division relatively close to the origin. For the testimages considered, about 65% of predictor vector values are, in absoluteterms, smaller than 20 and about 80% of predictor vector values aresmaller than 38. As depicted in FIG. 3, the quantization board (that isthe input vectorial space) is divided in main portions by consideringthe symmetry of the scattering of the values in the four quadrants, asmay be inferred by observing FIG. 2.

Through the vector mapping carried out in the block “Vector Mapping” ofFIG. 1, if values fall in the upper quadrants, no action is taken andthe first bit of the code is set to zero and if the predictor errorvalues to be compressed fall in the lower quadrants, the signs of theerror values e₁ and e₂ are changed. In this way a predictor error pairof values falling in the third quadrant will be quantized as if theywere in the first quadrant and similarly a predictor error pair ofvalues falling in the fourth quadrant will be quantized as they were inthe second quadrant. When such an inversion takes place, the first bitof the output code is set to 1, as will be described in more detaillater in this description.

As shown in the FIGS. 4 and 5, the upper portion of the quantizationboard (equivalent to the input vectorial space) is divided in regionsthat are shaped and distributed to minimize the quantization error. Eachregion has different dimensions and position on the quantization board.According to the sample embodiment considered and illustrated, everyregion is divided in 64 sub-regions of identical areas by dividing thehorizontal and vertical dimensions by eight, as depicted in FIG. 6.Therefore larger regions are subdivided in 64 larger areas for a heavierquantization, while smaller regions are subdivided in 64 smaller areasfor a lighter quantization with a bigger part of information beingpreserved uncompressed.

With reference to the indications shown in FIG. 6, each pair ofprediction error values E1, E2 is approximated by the nearest couple andcoding of the horizontal or X-coordinate takes place as follows: x_val =E1 − position_X_region[reg] + Half _X_step; x_code = x_val / X_step;if(x_code == 8) { x_val = E1 − position_X_region[reg][0]; x_code = x_val/ X_step; }

Position_X_region is the coordinate on the horizontal axis of the lowerleft point of the region. Position_Y_region is the coordinate on thevertical axis of the lower left point of the region. X_step is thequantization step in the horizontal direction given by:${X\_ Step} = \frac{RegionWidth}{8}$Y_step is the quantization step in the vertical direction given by:${Y\_ Step} = \frac{Region\_ Heigth}{8}$Half_X_step, Half_Y_step are the half of the quantization step and areused to provide the rounding feature in quantization.

If the point is near the upper limit of the region, the rounding featurewill associate the point with the next region (X_code=8). In this case,the half value is not added and a normal quantization is done.Evaluation of the quantized value is as follows:QE_(—)1=position_X_region[reg]+X_step*x_code;QE_(—)2=position_Y_region[reg]+Y_step*y_code.

Code Generation

The process of composition of the code is depicted in FIG. 7. Theinformation is encoded as follows: The point to be quantized is in theupper quadrants or not (1 bit); The region where the point falls (5bits); X-coordinate Code (3 bits); Y-coordinate Code (3 bits).

BP Decoding

The decoder scheme is depicted in FIG. 8. The decoding process includesa code evaluation operation performed in the block Code Evaluation ofFIG. 8 for the extraction of the compressed values that is the quantizederrors {overscore (e)}_(i), {overscore (e)}_(i+1) contained in themultibit code. The operation performed in the block “Inverse VectorMapping” assigns the correct sign to the quantized error values as afunction of the value of the one bit flag quadrant information containedin the multibit code.

Finally reconstruction of the input vector is done by adding the decodedquantized prediction errors of a certain sign to the predicted pair ofinput values as previously done during the compressing process andstored in the RAM block Memory.

Process Analysis

By considering the case in which prediction is made by using the last ofthe previously elaborated pair of input values, the prediction has nullcomputational cost and the DPCM is done with: Shift Add Div Mult CompAND 0 2 0 0 0 0

The cost of Vector Mapping and of Inverse Vector Mapping is: Shift AddDiv Mult Comp AND 0 0 0 0 16 0

The cost of region selection on the quantization board is: Shift Add DivMult Comp AND 0 0 0 0 16 0

Statistically 65% of the values are, in absolute terms, smaller than 20and in this case only 6 comparisons are needed. 80% of values are, inabsolute terms, smaller than 38 and the region is selected with 8comparisons.

Cost of Quantization: Shift Add Div Mult Comp AND 5 6 4 2 4 4

In the worst case, the computational cost of compression, for each pairof pixels, is: Shift Add Div Mult Comp AND 5 8 4 2 22 8

Therefore, the cost per pixel is: Shift Add Div Mult Comp AND 3 4 2 1 114

The cost for decompression is the cost for the reconstruction of thequantized values from the “horizontal” and “vertical coordinates” plusthe cost for the “Inverse Vector Mapping” and the cost for thereconstruction of values in the DPCM loop.

Totally for each pairs of pixels the cost is: Shift Add Div Mult CompAND 0 4 0 2 1 2

The computational cost for pixel is: Shift Add Div Mult Comp AND 0 2 0 11 1

The memory requirement for the storage of constant values is: 17 <=vqsnr < 21 Marginal 21 <= vqsnr < 25 Passable 25 <= vqsnr < 29 Normal 29<= vqsnr < 33 Good 33 <= vqsnr < 37 Fine 37 <= vqsnr Excellent

The performance of the novel method of this invention (DPCM+VQ), underthe above test conditions and standard sets of test images, was comparedwith the performance of a common vector quantization compression method(VQ) and with a commercially used compression method of Nokia.

For each region: Region Position (Hor. and Vert. coordinates) 22 bitsRegion Dimension 22 bits Region Quantization Step 22 bits Totally, forall the 32 regions of the 2112 bits example considered: Thresholds forthe regions (24 × 11 bits) 264 bits Values to bind thresholds to regions119 bits Grand Total 2971 bits (2.9 Kbits).

Notwithstanding the reduced computational cost per pixel of the novelmethod and implementing hardware architecture of this invention, interms of PSNR, the method of this invention compared favorably andgenerally yielded a better performance than known methods.

The algorithm has been tested on a set of about 100 images, acquired bya CMOS-VGA sensor at different light conditions. Experimental resultsshowed that the compression does not involve a perceptible loss ofquality in the output. As a result, the bit rate is low (a compressionof 40% of the input data is achieved) and the distortion is also keptlow (about 50 dB PSNR). Such results, joined with the low resourcerequirements, make the process more efficient than the most commonapproaches described in literature. This method may preferably beimplemented with a software computer program performing the method stepspreviously described when run on a computer.

1-9. (canceled)
 10. A method of compressing image Bayer pattern pixeldata to be successively processed to a full color image through colorinterpolation of each pixel of the full color image, the methodcomprising: dividing the Bayer data in three channels of pixel dataincluding a channel for each basic color filtered pixels of the image;separately compressing each channel data according to a compressionprocess comprising for each color channel, gathering Bayer pattern pixelvalues for processing by pairs, each pair including two successivepixels belonging to a same channel along a image pixel scanningdirection, each pair of values representing a currently processed inputvector, calculating a predictor vector of the input vector based upondifferences between the values defining the input vector and a pair ofprediction values representing a prediction error, quantizing eachcalculated predictor vector according to one of a heavier and lighterdegree of quantization depending on whether the predictor vector isrepresentative of an area of relatively uniform color of the image or anarea of relatively abrupt changes of colors of the image, and generatinga multibit code representative of the quantized predictor vector of theinput vector based upon a compression ratio.
 11. The method of claim 10,wherein quantizing comprises determining a type of image area of whichthe predictor vector is representative by dividing a vectorial space ofthe input vector into regions subdivided in an identical number of equalareas, having increasingly larger dimensions as they are located fartherfrom the origin, and by mapping each prediction vector to a targetvector of a set of target vectors, according to the region on which thepredictor vector falls.
 12. The method of claim 10, wherein the imagepixel scanning direction comprises a linewise raster mode image pixelscanning direction.
 13. The method of claim 10, wherein the pair ofprediction values for processing a next pair of gathered pixel valuescorrespond to the values of the current pair of gathered pixel values.14. The method of claim 10, wherein the pair of prediction values forprocessing a next pair of gathered pixel values correspond to a secondvalue of the current pair of gathered pixel values.
 15. The method ofclaim 10, wherein only upper quadrants of the input vectorial space aredivided in the regions on which to map each predictor vector byrecording a sign inversion of the respective prediction errors valuesfor the input prediction vectors falling in third and fourth quadrants,in a one bit flag of the multibit code.
 16. The method of claim 10,wherein information of each pair of Bayer pattern pixel data is encodedin the multibit code including: one bit flag indicating whether theprediction error vector maps in a upper or lower quadrant of the inputvectorial space; five bits identifying the region of division of theupper quadrants of the input vectorial space on which the predictionvector falls; three bits representing an X-coordinate of the predictionvector; and three bits representing a Y-coordinate of the predictionvector.
 17. A method of decompressing image Bayer pattern pixel datasplit into three channels of pixel data including a channel for eachbasic color filtered pixels of the image, the method comprising for eachcolor channel: evaluating a multibit code representative of a quantizedpredictor vector of an input vector based upon a compression ratio;decoding a calculated predictor vector quantized according to one of aheavier and lighter degree of quantization depending on whether thepredictor vector is representative of an area of relatively uniformcolor of the image or an area of relatively abrupt changes of colors ofthe image; and reconstructing an input vector based upon addition of thedecoded quantized prediction errors and a pair of prediction valuesrepresenting a prediction error, the input vector indicating a pair ofvalues including two successive pixels belonging to a same channel alongan image pixel scanning direction.
 18. The method of claim 17, whereinthe predictor vector was quantizing by determining a type of image areaof which the predictor vector is representative by dividing a vectorialspace of the input vector into regions subdivided in an identical numberof equal areas, having increasingly larger dimensions as they arelocated farther from the origin, and by mapping each prediction vectorto a target vector of a set of target vectors, according to the regionon which the predictor vector falls.
 19. The method of claim 17, whereinthe image pixel scanning direction comprises a linewise raster modeimage pixel scanning direction.
 20. The method of claim 17, wherein onlyupper quadrants of the input vectorial space are divided in the regionson which each predictor vector was mapped by recording a sign inversionof the respective prediction errors values for the input predictionvectors falling in third and fourth quadrants, in a one bit flag of themultibit code.
 21. The method of claim 17, wherein information of eachpair of Bayer pattern pixel data is encoded in the multibit codeincluding: one bit flag indicating whether the prediction error vectormaps in a upper or lower quadrant of the input vectorial space; fivebits identifying the region of division of the upper quadrants of theinput vectorial space on which the prediction vector falls; three bitsrepresenting an X-coordinate of the prediction vector; and three bitsrepresenting a Y-coordinate of the prediction vector.
 22. An imageprocessor for compressing image Bayer pattern pixel data to besuccessively processed to a full color image through color interpolationof each pixel of the full color image, the processor comprising: animage divider to divide the Bayer data in three channels of pixel dataincluding a channel for each basic color filtered pixels of the image;and an image compressor to separately compress each channel dataaccording to a compression process comprising for each color channel,gathering Bayer pattern pixel values for processing by pairs, each pairincluding two successive pixels belonging to a same channel along aimage pixel scanning direction, each pair of values representing acurrently processed input vector, calculating a predictor vector of theinput vector based upon differences between the values defining theinput vector and a pair of prediction values representing a predictionerror, quantizing each calculated predictor vector according to one of aheavier and lighter degree of quantization depending on whether thepredictor vector is representative of an area of relatively uniformcolor of the image or an area of relatively abrupt changes of colors ofthe image, and generating a multibit code representative of thequantized predictor vector of the input vector based upon a compressionratio.
 23. The processor of claim 22, wherein quantizing comprisesdetermining a type of image area of which the predictor vector isrepresentative by dividing a vectorial space of the input vector intoregions subdivided in an identical number of equal areas, havingincreasingly larger dimensions as they are located farther from theorigin, and by mapping each prediction vector to a target vector of aset of target vectors, according to the region on which the predictorvector falls.
 24. The processor of claim 22, wherein the image pixelscanning direction comprises a linewise raster mode image pixel scanningdirection.
 25. The processor of claim 22, wherein the pair of predictionvalues for processing a next pair of gathered pixel values correspond tothe values of the current pair of gathered pixel values.
 26. Theprocessor of claim 22, wherein the pair of prediction values forprocessing a next pair of gathered pixel values correspond to a secondvalue of the current pair of gathered pixel values.
 27. The processor ofclaim 22, wherein only upper quadrants of the input vectorial space aredivided in the regions on which to map each predictor vector byrecording a sign inversion of the respective prediction errors valuesfor the input prediction vectors falling in third and fourth quadrants,in a one bit flag of the multibit code.
 28. The processor of claim 22,wherein information of each pair of Bayer pattern pixel data is encodedin the multibit code including: one bit flag indicating whether theprediction error vector maps in a upper or lower quadrant of the inputvectorial space; five bits identifying the region of division of theupper quadrants of the input vectorial space on which the predictionvector falls; three bits representing an X-coordinate of the predictionvector; and three bits representing a Y-coordinate of the predictionvector.